Using OWA fuzzy operator to merge retrieval system results

With rapid growth of information sources, it is essential to develop methods that retrieve most relevant information according to the user requirements. One way of improving the quality of retrieval is to use more than one retrieval engine and then merge the retrieved results and show a single ranked list to the user. There are studies that suggest combining the results of multiple search engines will improve ranking when these engine are treated as independent experts. In this study, we investigated performance of Persian retrieval by merging four different language modeling methods and two vector space models with Lnu.ltu and Lnc.btc weighting schemes. The experiments were conducted on a large Persian collection of news archives called Hamshari Collection. Different variations of the Ordered Weighted Average (OWA) fuzzy operators method, called a quantifier based OWA operator and a degree-of-importance based OWA operator method have been tested for merging the results. Our experimental results show that the OWA operators produce better precision and ranking in comparison with weaker retrieval methods. But in comparison with stronger retrieval models they only produce minimal improvements

Urban geo big data

The paper deals with the general presentation of the Urban GEO BIG DATA, a collaborative acentric and distributed Free and Open Source (FOS) platform consisting of several components: local data nodes for data and related service Web deploy; a visualization node for data fruition; a catalog node for data discovery; a CityGML modeler; data-rich viewers based on virtual globes; an INSPIRE metadata management system enriched with quality indicators for each dataset.Three use cases in five Italian cities (Turin, Milan, Padua, Rome, and Naples) are examined: 1) urban mobility; 2) land cover and soil consumption at different resolutions; 3) displacement time series. Besides the case studies, the architecture of the system and its components will be presented

Fuzzy Order-of-Magnitude Based Link Analysis for Qualitative Alias Detection

Numerical link-based similarity techniques have proven effective for identifying similar objects in the Internet and publication domains. However, for cases involving unduly high similarity measures, these methods usually generate inaccurate results. Also, they are often restricted to measuring over single properties only. This paper presents an order-of-magnitude based similarity mechanism that integrates multiple link properties to derive semantic-rich similarity descriptions. The approach extends conventional order-of-magnitude reasoning with the theory of fuzzy sets. The inherent ability of this work in computing-with-words also allows coherent interpretation and communication within a decision-making group. The proposed approach is applied to supporting the analysis of intelligence data. When evaluated over a difficult terrorism-related dataset, experimental results show that the approach helps to partly resolve the problem of false positives

Fuzzy Order-of-Magnitude Based Link Analysis for Qualitative Alias Detection

Numerical link-based similarity techniques have proven effective for identifying similar objects in the Internet and publication domains. However, for cases involving unduly high similarity measures, these methods usually generate inaccurate results. Also, they are often restricted to measuring over single properties only. This paper presents an order-of-magnitude based similarity mechanism that integrates multiple link properties to derive semantic-rich similarity descriptions. The approach extends conventional order-of-magnitude reasoning with the theory of fuzzy sets. The inherent ability of this work in computing-with-words also allows coherent interpretation and communication within a decision-making group. The proposed approach is applied to supporting the analysis of intelligence data. When evaluated over a difficult terrorism-related dataset, experimental results show that the approach helps to partly resolve the problem of false positives